This survey characterizes an emerging research area, sometimes called coordination theory, that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology. A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints, and tank/subtask dependencies. Section 3 summarizes ways of applying a coordination perspective in three different domains: (1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research

Abstract-One measure of usefulness of a general-purpose distrib-uted computing system is the system’s ability to provide a level of per-formance commensurate to the degree of multiplicity of resources pres-ent in the system. Many different approaches and metrics of performance have been proposed in an attempt to achieve this goal in existing systems. In addition, analogous problem formulations exist in other fields such as control theory, operations research, and produc-tion management. However, due to the wide variety of approaches to this problem, it is difficult to meaningfully compare different systems since there is no uniform means for qualitatively or quantitatively eval-uating them. It is difficult to successfully build upon existing work or identify areas worthy of additional effort without some understanding of the relationships between past efforts. In this paper, a taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mecha-nism necessary in addressing this problem. The taxonomy, while pre-sented and discussed in terms of distributed scheduling, is also appli-cable to most types of resource management. As an illustration of the usefulness of the taxonomy an annotated bibliography is given which classifies a large number of distributed scheduling approaches accord-ing to the taxonomy. Index Terms-Distributed operating systems, distributed resource management, general-purpose distributed computing systems, sched-uling, task allocation, taxonomy. T I.

Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multi-agent scheduling of people and/or machines; distributed diagnosis of errors in local-area or telephone networks; concurrent engineering; `software agents' for information gathering. The design of coordination mechanisms for group...

Powerful workstations have become widely available as sources of computing cycles.

In a heterogeneous distributed real--time system, some nodes may experience more task arrivals than others, or tasks arrived at some nodes may have tighter laxities than those arrived at other nodes. In such an environment, transferring an unguaranteed task on a node to another node currently with the most abundant resources is not necessarily the best decision. We propose a new load sharing (LS) algorithm for real--time applications which takes into account the effect of future task arrivals on locating the best receiver for each unguaranteed task. Upon arrival of a task at a node, the node first checks whether or not it can complete the task in time using the minimum--laxity--first--served discipline. If the node cannot guarantee the arrived task or some of existing guarantees were to be invalidated as a result of inserting the task into its queue, then the node must locate a remote node to which each unguaranteed task will be transferred. The proposed LS algorithm minimizes not only...

We consider the problem of generating a large state-space in a distributed fashion. Unlike previously proposed solutions that partition the set of reachable states according to a hashing function provided by the user, we explore heuristic methods that completely automate the process. The first step is an initial random walk through the state space to initialize a search tree, duplicated in each processor. Then, the reachability graph is built in a distributed way, using the search tree to assign each newly found state to classes assigned to the available processors. Furthermore, we explore two remapping criteria that attempt to balance memory usage or future workload, respectively. We show how the cost of computing the global snapshot required for remapping will scale up for system sizes in the foreseeable future. An extensive set of results is presented to support our conclusions that remapping is extremely beneficial. 1 Introduction Discrete systems are frequently analyzed by genera...

In a distributed real-time system, uneven task arrivals temporarily overload some nodes while leaving others idle or underloaded. Consequently, some tasks may miss their deadlines even if the overall system has the capacity to meet the deadlines of all tasks. An effective load sharing (LS) scheme is proposed as a solution to this problem. Upon arrival of a task at a node, the node determines whether or not the node can complete the task in time under the minimum--laxity--first--served policy. If the task cannot be guaranteed or if guarantees of some other tasks are to be violated due to the addition of this task to the existing schedule, the node looks up the list of loss--minimizing decisions , and determines the best node among a set of nodes in its physical proximity, called its buddy set , to which the task(s) may be transferred. This list of decisions is periodically updated using Bayesian decision analysis and prior/posterior state distributions. These probability distributions a...

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A large class of computations are characterized by a sequence of phases, with phase changes occurring unpredictably. We consider the decision problem regarding the remapping of workload to processors in a parallel computation when (i) Ihe uiility of remapping md the future behavior of the workload is uncertain, and (ii) phases exhibit stable execution requirements during a given phase, but requirements may change radically between phases. For these problems a workload assignment gen-erated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. We address the fundamental prob-lem of balancing the expected remapping performance gain against the delay cost. Stochastic dynamic programming is used to show that the remapping decision policy minimizing the expected running time of the computation has an extremely simple structure: the optimal decision at any decision step is followed by comparing the probability of remapping gain against a threshold. However, threshold calculation requires a priori estimation of the performance gain achieved by remap ping. Because this gain may not be predictable, we examine the performance of a heuristic policy that does not require estimation of the gain. In most cases we find nearly optimal performance if remapping